AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments
This addresses the problem of enabling spacecraft to navigate autonomously around small celestial bodies, which is incremental as it builds on existing SLAM techniques with domain-specific enhancements.
The authors tackled autonomous navigation near unknown small celestial bodies by proposing AstroSLAM, a vision-based SLAM method that incorporates orbital motion priors into a factor graph, achieving improved performance over a baseline solution in experiments using real mission data and simulator imagery.
We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate the excellent performance of AstroSLAM using both real legacy mission imagery and trajectory data courtesy of NASA's Planetary Data System, as well as real in-lab imagery data generated on a 3 degree-of-freedom spacecraft simulator test-bed.